A medical imaging system configured to link acquired images to markers or tags on an anatomical illustration, based, at least in part on spatial and anatomical data associated with the acquired image. The medical imaging system may be further configured to generate a diagnostic report including the anatomical illustration containing the markers. The diagnostic report may allow a user to select a marker to view information associated with an acquired image and/or the acquired image. Multiple images may be associated with a marker, and/or multiple markers may be associated with an image. A set of 2D and/or 3D anatomical illustrations may be generated which contains markers from multiple diagnostic reports and updated automatically for an individual patient's anatomical model by the application to reflect measurements and/quantitative findings related to organ, tissue, and vessel size, location, deformation, and/or obstruction.

The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

A movement of an area whose shape changes for each respiration or for each heartbeat is displayed. A diagnosis support program that analyzes images of a human body and displays analysis results, the program causing a computer to execute: processing of acquiring a plurality of frame images from a database that stores the images (S1); processing of specifying a respiratory cycle based on pixels in a specific area in each of the frame images (S2); processing of detecting a lung field based on the specified respiratory cycle (S3); processing of dividing the detected lung field into a plurality of block areas (S4) and calculating a change in image in a block area in each of the frame images (S5); processing of performing a Fourier analysis of a change in image in each block area in each of the frame images (S6); and processing of displaying each image after the Fourier analysis on a display as a pseudo color image (S7).

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

A surgical image acquisition system includes multiple illumination sources, each source emitting light at a specified wavelength, a light sensor to receive light reflected from a tissue sample illuminated by each of the illumination sources, and a computing system. The computer system may receive data from the light sensor when the tissue sample is illuminated by the illumination sources, determine a depth of a structure within the tissue sample, and calculate visualization data regarding the structure and its depth within the tissue. The visualization data may have a format for use by a display system. The structure may include vascular tissue. The illumination sources may include red, green, blue, infrared, ultraviolet, and white light sources. The structure depth may be determined by a spectroscopy method or a Doppler shift method. The system may include a controller and computer enabled instructions to accomplish the above.

Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.